1887

Abstract

Summary

To design a successful EOR scenario, it is crucial to have the accurate knowledge of the IFT of the EOR agents and the reservoir fluid at a wide range of pressure and temperature. Therefore, it is necessary to model IFT at the full range of conditions through which the injected and the reservoir fluid pass on their way in porous media. In this study, a robust artificial intelligent-based model (COA-LSSVM) has been developed to predict IFT of methane/normal alkane systems as a function of pressure, temperature, the difference of densities and difference of viscosity of two components of the mixture. The results indicate that COA-LLSVM is an accurate and reliable approach to model IFT. Moreover, using the difference of viscosity, which is an indicator of tensile stress and friction in the surface of two phases, as an additional input for modelling the IFT, can improve the results. This approach is highly recommended, especially for modelling the dynamic IFT, and can results in improving simulation of EOR methods that wettability alteration is their dominant mechanism.

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/content/papers/10.3997/2214-4609.201801709
2018-06-11
2024-04-24
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